hMRI - A toolbox for quantitative MRI in neuroscience and clinical research
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hMRI - A toolbox for quantitative MRI in neuroscience and clinical research. / Tabelow, Karsten; Balteau, Evelyne; Ashburner, John; Callaghan, Martina F; Draganski, Bogdan; Helms, Gunther; Kherif, Ferath; Leutritz, Tobias; Lutti, Antoine; Phillips, Christophe; Reimer, Enrico; Ruthotto, Lars; Seif, Maryam; Weiskopf, Nikolaus; Ziegler, Gabriel; Mohammadi, Siawoosh.
In: NEUROIMAGE, Vol. 194, 01.07.2019, p. 191-210.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - hMRI - A toolbox for quantitative MRI in neuroscience and clinical research
AU - Tabelow, Karsten
AU - Balteau, Evelyne
AU - Ashburner, John
AU - Callaghan, Martina F
AU - Draganski, Bogdan
AU - Helms, Gunther
AU - Kherif, Ferath
AU - Leutritz, Tobias
AU - Lutti, Antoine
AU - Phillips, Christophe
AU - Reimer, Enrico
AU - Ruthotto, Lars
AU - Seif, Maryam
AU - Weiskopf, Nikolaus
AU - Ziegler, Gabriel
AU - Mohammadi, Siawoosh
N1 - Copyright © 2019. Published by Elsevier Inc.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
AB - Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
KW - Journal Article
U2 - 10.1016/j.neuroimage.2019.01.029
DO - 10.1016/j.neuroimage.2019.01.029
M3 - SCORING: Journal article
C2 - 30677501
VL - 194
SP - 191
EP - 210
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
ER -